Cache Consistency AGENTS.md Template for AI Coding Agents

Overview

Direct answer: This AGENTS.md template defines the operating context for a cache consistency strategy using AI coding agents, covering both single-agent workflows and multi-agent orchestration with explicit handoffs and governance rules.

The AGENTS.md template codifies roles, responsibilities, source-of-truth rules, and escalation paths so teams can operate the cache layer with clear guardrails and auditability. It enables scalable coordination across planners, implementers, validators, and reconciliers while maintaining strong tool governance and human review where needed.

When to Use This AGENTS.md Template

• Initialize a project-level operating context for maintaining coherence across distributed caches using AI agents.
• Document agent roster, roles, handoffs, and escalation paths before implementing code.
• Capture memory, sources of truth, and permission rules to prevent context drift and architecture drift.
• Define validation, testing, deployment, and rollback procedures for cache-related changes.

Copyable AGENTS.md Template

# AGENTS.md

Project: Cache Consistency Strategy with AI Coding Agents

Scope
- This AGENTS.md template defines how to use AI coding agents to maintain cache coherence across distributed systems, including multi-agent orchestration and handoffs.

Roles
- Planner: designs cache key strategy, invalidation events, and coherence checks.
- Implementer: implements cache read/write paths, expiry policies, and refresh logic.
- Validator: validates state coherence against the source of truth after writes.
- Reconciler: monitors drift, reconciles stale data, and triggers refresh.
- Domain Specialist: provides data-domain-specific caching invariants and metrics.

Supervisor/Orchestrator
- The Orchestrator coordinates tasks, enforces idempotency, propagates events, and records decisions in a central context store.

Handoff Rules
- Planner ➜ Implementer: transfer plan with explicit key formats, TTLs, and invariants.
- Implementer ➜ Validator: provide evidence of coherence after operation.
- Validator ➜ Reconciler: if drift detected, trigger corrective flow; escalate if unresolved.
- Orchestrator: maintains a single source of truth for plan status and retry policy.

Context, Memory, and Source-of-Truth
- Source of Truth: primary data store used to validate cache content.
- Cache layers: L1 (local), L2 (distributed), L3 (optional external cache).
- Memory: per-agent ephemeral state; all persistent state stored in the central context log.

Tool Access and Permission Rules
- Access: cache APIs (read/write/invalidate), data stores (read), secrets manager (read), observability (read).
- Permissions: roles constrain actions; no agent may access production systems outside approved endpoints.

Architecture Rules
- Use a multi-layer cache architecture with event-driven invalidation.
- Every write must emit an invalidation or refresh signal to downstream caches.
- Operations must be idempotent and replay-safe.

File Structure Rules
- infra/cache/: cache adapters
- agents/planner/: planner agent code
- agents/implementer/: implementer agent code
- agents/validator/: validator agent code
- agents/reconciler/: reconciler agent code
- domain/specific/: domain invariants and metrics
- tests/: tests for cache coherence flows

Data, API, or Integration Rules
- Keys: standardized prefix, versioning, and namespacing.
- TTLs: explicit TTLs and refresh rules.
- Ingress/egress: validate data formats before caching.

Validation Rules
- Coherence invariant: cache value equals source-of-truth after write.
- Staleness budget: maximum allowed staleness defined and enforced.
- Drift detection: monitor discrepancy between caches and source-of-truth.

Security Rules
- Secrets in Secrets Manager; no plaintext credentials in logs.
- Encrypt at rest; access controls on cache endpoints.
- Audit logging for all write and refresh actions.

Testing Rules
- Unit tests for key helpers (key formatting, TTL calculation).
- Integration tests verifying end-to-end coherence across layers.
- End-to-end tests with simulated drift and recovery.

Deployment Rules
- CI gates with cache-invalidation tests; canary rollout for cache changes.
- Rollback plan to revert to previous cache state.

Human Review and Escalation Rules
- Trigger human review if drift persists after N retries or if invariants fail > M% of observations.

Failure Handling and Rollback Rules
- On failure: revert cache to last known-good state from source-of-truth and re-run coherence checks.
- Maintain an audit trail of all changes for traceability.

Things Agents Must Not Do
- Do not bypass the source-of-truth validation.
- Do not cache writes without successful validation.
- Do not expose secrets in logs or messages.
- Do not perform production changes outside approved channels.

Recommended Agent Operating Model

Roles, responsibilities, decision boundaries, and escalation paths are defined to support both single-agent and multi-agent orchestration for cache consistency. The Planner proposes a plan; the Implementer executes; the Validator confirms coherency; the Reconciler maintains long-term consistency; Domain Specialists provide data-domain invariants. Escalation paths ensure timely human review when confidence is below threshold.

Recommended Project Structure

ai-cache-consistency/
  infra/
    cache/
  agents/
    planner/
    implementer/
    validator/
    reconciler/
  domain/
  tests/
  docs/
  configs/

Core Operating Principles

• Single source of truth remains the authoritative reference for coherence checks.
• All agent actions are idempotent and replay-safe.
• Clear ownership of cache keys, TTLs, and invalidation events.
• Maintain full traceability of decisions and handoffs.
• Enforce strict tool governance and access controls.

Agent Handoff and Collaboration Rules

• Planner → Implementer: handoff with concrete key names, TTLs, and expected invalidation strategies.
• Implementer → Validator: provide state snapshots, success criteria, and evidence of coherence.
• Validator → Reconciler: trigger drift repairs only when invariants fail beyond tolerance.
• Domain Specialist → Planner: update invariants based on data-domain changes.
• Orchestrator: enforce escalation if any agent misses a deadline or violates a guardrail.

Tool Governance and Permission Rules

• All cache operations must be authorized and auditable.
• Secrets access is restricted to the Secrets Manager, with read-only paths where possible.
• Production endpoints require approval gates and canary checks before full rollout.
• External service calls are rate-limited and logged with contextual metadata.

Code Construction Rules

• Use deterministic key generation with namespacing and versioning.
• Validate input types and formats before caching.
• Ensure idempotent write paths and safe retries.
• Separate concerns: planner logic from cache access logic.
• Include explicit tests for coherence invariants and drift detection.

Security and Production Rules

• Encrypt sensitive data at rest; rotate credentials regularly.
• Limit blast radius with compartmentalized caches and scoped permissions.
• Monitor and alert on anomaly in cache latency or drift frequency.

Testing Checklist

• Unit tests for key generation, cache ops, and invariants.
• Integration tests across Planner, Implementer, Validator, and Reconciler flows.
• End-to-end tests simulating drift and recovery.
• Performance tests for cache invalidation throughput.

Common Mistakes to Avoid

• Over-relying on a single agent for all steps; fail to model multi-agent handoffs.
• Skipping validation or bypassing source-of-truth checks.
• Granting broad privileges to cache endpoints without controlling access.
• Inadequate logging of decisions and escalations.

Related implementation resources: AI Use Case for Corporate Event Managers Using Slack To Orchestrate Day-Of Venue Tasks Across Multi-Department Teams and AI Agent Use Case for Wholesalers Using Multi-Currency Ledger Trackers To Calculate Foreign Exchange Risk Exposure Across Global Accounts.

FAQ

What is the purpose of this AGENTS.md template for cache consistency?

It codifies roles, handoffs, and guardrails to coordinate AI coding agents for coherent cached data across distributed systems.

How do agents handle cache invalidation and refresh?

Invalidation events propagate through the orchestrator to all relevant caches with versioned keys; refreshes pull from the source of truth as needed and re-validate coherence.

What are the escalation paths if drift remains after retries?

Drift beyond tolerance triggers human review, a rollback plan, and potential re-architecting of the cache strategy with updated invariants.

How are handoffs between Planner, Implementer, and Validator managed?

Handoffs require explicit plan metadata, evidence of success criteria, and timestamps; the orchestrator enforces ordering and retries when needed.

What constitutes a successful coherence check?

Cache state matches the source of truth within the defined tolerance, and all caches reflect the latest committed write with valid TTLs.